identifying_quantifying_financial_bubbles_hyped_log_period_p
Unverified ML strategy on Multi by chirindaopensource. BotFinder score 18 out of 100.
An end-to-end Python implementation of Cao et al.'s (2025) HLPPL methodology for the identification of financial (asset price) bubbles. Implements 7-parameter Log-Periodic Power La
Source: github
BotFinder analysis pending.
identifying_quantifying_financial_bubbles_hyped_log_period_p
README.md Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model --- Repository: https://github.com/chirindaopensource/identifyingquantifyingfinancialbubbleshypedlogperiodpowerlaw Owner: 2025 Craig Chirinda (Open Source Projects) This repository contains an independent, professional-grade Python implementation of the research methodology from the 2025 paper entitled "Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model" by: Zheng Cao Xingran Shao Yuheng Yan Helyette Geman The project provides a complete, end-to-end computational framework for replicating the paper's findings. It delivers a modular, auditable, and extensible pipeline that executes the entire research workflow: from rigorous data validation and NLP feature engineering to LPPL model fitting, deep learning, and backtesting. Table of Contents - Introduction - Theoretical Background - Features - Methodology Implemented - Core Components (Notebook Structure) - Key Callable: executefullstudy - Prerequisites - Installation - Input Data Structure - Usage -
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)